Article

Covariate adjustment in family-based association studies

Department of Epidemiology, Columbia University, New York, New York, United States
Genetic Epidemiology (Impact Factor: 2.95). 04/2005; 28(3):244-55. DOI: 10.1002/gepi.20055
Source: PubMed

ABSTRACT Family-based tests of association between a candidate locus and a disease evaluate how often a variant allele at the locus is transmitted from parents to offspring. These tests assume that in the absence of association, an affected offspring is equally likely to have inherited either one of the two homologous alleles carried by a parent. However, transmission distortion was documented in families in which the offspring are unselected for phenotype. Moreover, if offspring genotypes are associated with a risk factor for the disease, transmission distortion to affected offspring can occur in the absence of a causal relation between gene and disease risk. We discuss the appropriateness of adjusting for established risk factors when evaluating association in family-based studies. We present methods for adjusting the transmission/disequilibrium test for risk factors when warranted, and we apply them to data on CYP19 (aromatase) genotypes in nuclear families with multiple cases of breast cancer. Simulations show that when genotypes are correlated with risk factors, the unadjusted test statistics have inflated size, while the adjusted ones do not. The covariate-adjusted tests are less powerful than the unadjusted ones, suggesting the need to check the relationship between genotypes and known risk factors to verify that adjustment is needed. The adjusted tests are most useful for data containing a large proportion of families that lack disease-discordant sibships, i.e., data for which multiple logistic regression of matched sibships would have little power. Software for performing the covariate-adjusted tests is available at http://www.stanford.edu/dept/HRP/epidemiology/COVTDT.

0 Followers
 · 
72 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: With the establishment of large consortiums of researchers, genome-wide association (GWA) studies have become increasingly popular and feasible. Although most of these association studies focus on unrelated individuals, a lot of advantages can be exploited by including families in the analysis as well. To overcome the additional genotyping cost, multi-stage designs are particularly useful. In this article, I offer a perspective view on genome-wide family-based association analyses, both within a model-based and model-free paradigm. I highlight how multi-stage designs and analysis techniques, which are quite popular in clinical epidemiology, can enter GWA settings. I furthermore discuss how they have proven successful in reducing analysis complexity, and in overcoming one of the most cumbersome statistical hurdles in the genome-wide context, namely controlling increased false positives due to multiple testing.
    Statistics in Medicine 08/2011; 30(18):2201-21. DOI:10.1002/sim.4259 · 2.04 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In order to study family-based association in the presence of linkage, we extend a generalized linear mixed model proposed for genetic linkage analysis (Lebrec and van Houwelingen (2007), Human Heredity 64, 5-15) by adding a genotypic effect to the mean. The corresponding score test is a weighted family-based association tests statistic, where the weight depends on the linkage effect and on other genetic and shared environmental effects. For testing of genetic association in the presence of gene-covariate interaction, we propose a linear regression method where the family-specific score statistic is regressed on family-specific covariates. Both statistics are straightforward to compute. Simulation results show that adjusting the weight for the within-family variance structure may be a powerful approach in the presence of environmental effects. The test statistic for genetic association in the presence of gene-covariate interaction improved the power for detecting association. For illustration, we analyze the rheumatoid arthritis data from GAW15. Adjusting for smoking and anti-cyclic citrullinated peptide increased the significance of the association with the DR locus.
    Biometrical Journal 02/2010; 52(1):22-33. DOI:10.1002/bimj.200900057 · 1.24 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The study of gene-environment interactions is an increasingly important aspect of genetic epidemiological investigation. Historically, it has been difficult to study gene-environment interactions using a family-based design for quantitative traits or when parent-offspring trios were incomplete. The QBAT-I provides researchers a tool to estimate and test for a gene-environment interaction in families of arbitrary structure that are sampled without regard to the phenotype of interest, but is vulnerable to inflated type I error if families are ascertained on the basis of the phenotype. In this study, we verified the potential for type I error of the QBAT-I when applied to samples ascertained on a trait of interest. The magnitude of the inflation increases as the main genetic effect increases and as the ascertainment becomes more extreme. We propose an ascertainment-corrected score test that allows the use of the QBAT-I to test for gene-environment interactions in ascertained samples. Our results indicate that the score test and an ad hoc method we propose can often restore the nominal type I error rate, and in cases where complete restoration is not possible, dramatically reduce the inflation of the type I error rate in ascertained samples. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 01/2014; 33(2). DOI:10.1002/sim.5930 · 2.04 Impact Factor